Time-frequency analysis of electrocardiograms

ABSTRACT

Electrocardiograms can be analyzed in the time-frequency domain, following conversion into time-frequency maps, to determine characteristics or features of various waveforms, such as waveform morphology and/or the amplitude(s) and location(s) (in time and/or frequency) of one or more extrema of the waveform. Based on comparison of the extrema against thresholds and/or against each other, disease conditions may be determined.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 62/556,907, filed on Sep. 11, 2017 and entitled“Time-Frequency Analysis of Electrocardiogram,” which is herebyincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to cardiac diagnostics,specifically to systems, devices, and methods for electrocardiogram(ECG) analysis.

BACKGROUND

Heart testing for coronary heart disease, myocardial ischemia, and otherabnormal heart conditions is routinely performed using ECGs, whichrepresent electrical potentials reflecting the electrical activity ofthe heart measured via electrodes placed on the patient's skin. Theheart's electrical system controls timing of the heartbeat by sending anelectrical signal through the cells of the heart. The heart includesconducting cells for carrying the heart's electrical signal, and musclecells that contract the chambers of the heart as triggered by theheart's electrical signal. The electrical signal starts in a group ofcells at the top of the heart called the sinoatrial (SA) node. Thesignal then travels down through the heart, conducting cell toconducting cell, triggering first the two atria and then the twoventricles. Simplified, each heartbeat occurs by the SA node sending outan electrical impulse. The impulse travels through the upper heartchambers, called “atria,” electrically depolarizing the atria andcausing them to contract. The atrioventricular (AV) node of the heart,located on the interatrial septum close to the tricuspid valve, sends animpulse into the lower chambers of the heart, called “ventricles,” viathe His-Purkinje system, causing depolarization and contraction of theventricles. Following the subsequent repolarization of the ventricles,the SA node sends another signal to the atria to contract, restartingthe cycle. This pattern, and variations therein as may result, e.g.,from an abnormality in heart function, can, in principle, be captured inan ECG, allowing medically trained personnel to draw inferences aboutthe heart's condition from the ECG. However, subtleties and complexitiesin the signals, variations between signals for different leads(corresponding to different electrodes or combinations of electrodes),and/or low signal levels for features of interest can render the readingand interpretation of the ECG difficult, and hinder an unambiguous andaccurate diagnosis. For example, developing abnormalities are often notimmediately visible in an ECG as analyzed in a conventional manner,which causes many patients to be misdiagnosed as healthy.

SUMMARY

Described herein, in various embodiments, are systems, devices, andmethods for enhancing the analysis of ECGs through advanced signalprocessing in the time-frequency domain, e.g., to resolve ambiguities inidentifying waveforms or other features within the signals and/orincrease the accuracy and/or diagnostic relevance of qualitativecharacterizations of and/or quantitative measures derived for thesefeatures.

In accordance with various embodiments, ECGs—i.e., time-domain signalsreflecting the electric potential of the heart throughout one or morecardiac cycles—are computationally converted, by a suitabletime-frequency transform, into respective two-dimensional time-frequencymaps. In a “time-frequency map,” as the term is herein broadlyunderstood, the signal value (corresponding, e.g., to a measuredelectric potential) is provided as a function of two independentvariables: time, and a measure of the spectral components of the signalsuch as, e.g., frequency (in the narrower sense) or a scaling factor.For example, in some embodiments, short-time Fourier transform is usedto convert the ECGs into spectrograms, where the signal value is afunction of time and frequency. In other embodiments, the ECGs areconverted by (continuous or discrete) wavelet transform into so-calledscalograms, where the signal value is a function of time and a scalingfactor. More generally, a filter bank may be used to transform the ECGinto a time-frequency representation. For ease of reference, thedimension of the time-frequency map that corresponds to the frequency orscaling factor (or any other measure of the spectral components) isherein generally referred to as the frequency dimension or simplyfrequency.

The time-frequency maps, by themselves or in conjunction with the ECGsfrom which they are derived, may be displayed to a physician (or otherclinical personnel) for interpretation, and/or analyzed automatically,e.g., to derive quantitative metrics of heart condition and functiontherefrom. By spreading out the spectral components of the measured ECGsignals, the time-frequency maps can visualize information not or noteasily and unambiguously discernible from the ECGs themselves, which canimprove the accuracy of diagnostic determinations and may even helpdetect conditions traditionally not diagnosed based on ECGs (such as,e.g., myocardial ischemia).

In various embodiments, the analysis of the time-frequency maps focuseson one or more types of waveforms in the signals that reflect cardiacactivity during certain phases of the cardiac cycle, such as, forexample, the T wave (which represents the repolarization of theventricles), the QRS complex (which corresponds to the depolarization ofthe ventricles), and the P wave (which corresponds to atrialdepolarization). Once a waveform of a particular type has beenidentified in the ECG and/or its associated time-frequency map, certainfeatures of the waveform, such as one or more local extrema across oneor two dimensions, may be determined in the time-frequency map, and theamplitude or position in time and/or frequency of the extrema may serveas quantitative measures. For example, in some embodiments, extrema aredetermined in the time-frequency map, within a time range correspondingto the waveform, across time and frequency. In other embodiments,extrema are determined across frequency only at one or more selectedpoints in time (or substantially across frequency within one or morenarrow (compared with the width of the time range corresponding to theentire waveform) time bands surrounding the selected points in time).These points (or bands) in time may be identified in the(one-dimensional) ECG, in one or more (likewise one-dimensional)sections through the time-frequency map taken at one or more particularfrequencies, or in selected frequency ranges, and may correspond, e.g.,to peaks of the waveforms or points preceding or following the peaks byspecified amounts. The number and types of extrema generally depends onthe morphology of the waveform, discriminating, for example, betweenmonophasic and biphasic waves and/or between single-peaked anddouble-peaked waves, and this morphology may be determined prior to andguide the search for the extrema. Following the determination of the oneor more measures quantitatively characterizing the waveform, thesemeasures may be compared against empirical thresholds, against eachother, and/or across leads to discover specific disease conditionsand/or compute one or more indices indicative of heart health.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will more readily understood from the followingdescription of various embodiments, in particular, when taken inconjunction with the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating an example system for ECGanalysis in accordance with various embodiments.

FIG. 2 is an example ECG, illustrating various waveforms and points intime used in accordance with various embodiments.

FIGS. 3A and 3B are graphs of an example ECG for a normal heart and ascalogram resulting from its wavelet transform, respectively, inaccordance with one embodiment.

FIGS. 3C and 3D are graphs of an example ECG for an abnormal heart and ascalogram resulting from its wavelet transform, respectively, inaccordance with one embodiment.

FIG. 4 is a flow chart of example methods for time-frequency analysis ofECGs in accordance with various embodiments.

FIG. 5 is a flow chart of an example method for determining waveformmorphology in accordance with various embodiments.

FIGS. 6A-6E are example scalograms, in accordance with variousembodiments, for five leads, taken for a diseased patient.

FIG. 7 is a perspective view of an example heart test device inaccordance with various embodiments.

FIG. 8 is a user interface diagram showing an example report screen inaccordance with various embodiments.

FIG. 9 is a block diagram of an example computer system as may serve asprocessing facility in accordance with various embodiments.

DESCRIPTION

FIG. 1 illustrates, in block-diagram form, various functional componentsof an example system for ECG analysis in accordance with variousembodiments. The system 100 includes one or more electrodes 102 foracquiring ECG signals (e.g., 10 electrodes for a traditional 12-leadECG), a processing facility 104 for processing the ECG signals, e.g., toobtain time-frequency maps and derive various measures therefrom, and anelectrode interface 106 connecting the electrodes 102 to the processingfacility 104. The electrode interface 106 includes circuitry thatoutputs electrical signals suitable as input to the processing facility104, e.g., by digitally sampling analog input signals. The system 100further includes a display device 108 for outputting the ECG testresults (including, e.g., the ECGs, time-frequency maps, and/or variousquantitative metrics and indices), and optionally other input/outputdevices 109, such as a keyboard and mouse and/or a printer, forinstance. The display device 108 may be a touchscreen doubling as auser-input device. The processing facility 104, electrode interface 106,display 108, and input/output devices 109 may be implemented as asingle, stand-alone device implementing all computational functionalityfor ECG signal processing and presentation. Alternatively, they may beprovided by the combination of multiple communicatively coupled devices.For example, an ECG test device with limited functionality for recordingand/or processing ECG signals received from one or more electrodes 102via an electrode interface 106 of the device may outsource certaincomputationally intense processing tasks to other computers with whichit is communicatively coupled via a wired or wireless network. Thus, thefunctionality of the processing facility 104 may be distributed betweenmultiple computational devices that communicate with each other. Whetherprovided in a single device or distributed, the processing facility 104may be implemented with dedicated, special-purpose circuitry (such as,e.g., a digital signal processor (DSP), field-programmable gate array(FPGA), analog circuitry, or other), a suitably programmedgeneral-purpose computer (including at least one processor andassociated memory), or a combination of both. Herein, the term “hardwareprocessor” is used in reference to both special-purpose circuitry andgeneral-purpose processors as used in general-purpose computers andconfigured via software.

The processing facility 104 may include various functionally distinctmodules, such as an ECG-signal-processing module 110 that generates ECGsfor multiple leads from the (e.g., digitally sampled) ECG signals fordisplay and analysis (e.g., by filtering, smoothing, scaling, etc., aswell as by combining signals for various leads as explained furtherbelow); a time-frequency transform module 112 that converts the ECG foreach lead into a two-dimensional time-frequency map (signed or unsigned)and, optionally, normalizes the time-frequency map; an analysis module116 that analyzes the ECGs and/or time-frequency maps to determinevarious quantitative measures and indices (which may involve, e.g.,identifying delimiters between successive cardiac cycles, determiningcertain waveforms (such as the QRS complex, T wave, P wave) or othersegments or features within the ECGs, determining quantitative measuresof the waveforms from the time-frequency maps, performing variouscomparisons of the determined measures, computing indices indicative ofheart condition and/or making qualitative diagnostic determinations,etc.), and/or a user-interface 118 module that generates graphicrepresentations of the data provided by the other modules and assemblesthem into a screen for display. The ECG-signal-processing module 110 maybe a conventional processing module as used in commercially availableheart monitors and/or as is capable of straightforward implementation byone of ordinary skill in the art. The time-frequency transform module112 and analysis module 116 implement algorithms and providefunctionality explained in detail below, and can be readily implementedby one of ordinary skill in the art given the benefit of the presentdisclosure.

As will be readily appreciated, the depicted modules reflect merely oneamong several different possibilities for organizing the overallcomputational functionality of the processing facility 104. The modulesmay, of course, be further partitioned, combined, or altered todistribute the functionality differently. The various modules may beimplemented as hardware modules, software modules executed by ageneral-purpose processor, or a combination of both. For example, it isconceivable to implement the time-frequency transform module 112, whichgenerally involves the same operations for each incoming ECG signal,with special-purpose circuitry to optimize performance, whileimplementing the analysis module 116 in software.

While the quantification of heart function in accordance herewith is, ingeneral, not limited to any particular number of electrodes, the system100 includes, in various embodiments, ten electrodes 102 to facilitateobtaining a standard twelve-lead ECG, as is routinely used in themedical arts. In accordance with the standard configuration, four of theten electrodes (conventionally labeled LA, RA, LL, RL) are placed on thepatient's left and right arms and legs; two electrodes (labeled V1 andV2) are placed between the fourth and fifth ribs on the left and rightside of the sternum; a further, single electrode (labeled V3) is placedbetween V2 and V4 on the fourth intercostal space; one electrode(labeled V4) is placed between the fifth and sixth ribs at themid-clavicular line (the imaginary reference line that extends down fromthe middle of the clavicle), and, in line therewith, another electrode(labeled V5) is positioned in the anterior axillary line (the imaginaryreference line running southward from the point where the collarbone andarm meet), and the tenth electrode (labeled V6) is placed on the samehorizontal line as these two, but oriented along the mid-axillary line(the imaginary reference point straight down from the patient's armpit).The electric potentials measured by electrodes V1 through V6 correspondto six of the twelve standard leads; the remaining six leads correspondto the following combinations of the signals measured with theindividual electrodes: I=LA−RA; II=LL−RA; III=LL−LA; aVR=RA−½ (LA+LL);aVLLA−½ (RA+LL); and aVF=LL−½ (RA+LA).

FIG. 2 schematically shows an example ECG 200 for a single cardiaccycle, illustrating the P wave 202, QRS complex 204 (which includes theRS segment 206), and T wave 208. As depicted, the electric potentialusually reaches its maximum 210 at R during the QRS complex 204.However, the polarity of the signal may be inverted (such that the Rpeak has a negative value). Further, in some ECG signals, the S peak hasa greater absolute value than the R peak. In fact, not every ECGunambiguously exhibits all the waveforms and features shown in the(rather typical) example ECG 200, or exhibits them with differentwaveform morphology. For example, while the depicted P wave 202 and Twave 208 each feature a single, positive peak, the waveforms may, indifferent ECGs (or different leads), be negative, double-peaked, orbiphasic (that is, transition between positive and negative values).This uncertainty can cause difficulty in ascertaining waveforms andfeatures, as well as in normalizing the signal based on a discretefeature of the ECG such as, e.g., the R peak. To circumvent thenormalization difficulty, various embodiments base normalization not ona specific waveform feature (e.g., peak), but on a signal maximum andminimum identified across a time range, such as the time intervalencompassing at least the RS segment 206 (and thus including both the Rand the S peak if they are, in fact, clearly represented in the signal),irrespective of the feature to which that maximum or minimum corresponds(if any).

FIG. 2 also illustrates certain points in time at which data isevaluated in accordance with various embodiments, such as the time 212at which the T wave 208 assumes its maximum, “early” and “late” times214, 216 bracketing the maximum of the T wave 208, and, similarly, thetime 218 at which the P wave 202 peaks and corresponding early and latetimes 220, 222 bracketing the maximum of the P wave 202. The early andlate times 214, 216, 220, 222 may be on the rising edge and falling edgeof the T wave 208 or P wave 202, respectively. In various embodiments,they are selected within ranges between the respective wave maximum andpoints in time preceding and following that wave maximum, respectively,at which the wave assumes some specified fraction, e.g., half, of itsmaximum value. For biphasic or double-peaked waveforms, relevant pointsin time for data evaluation may include, e.g., the respective (multiple)times at which the wave peaks in the positive or negative direction, thetime(s) at which the amplitude transitions between positive and negativevalues, the time corresponding to the “dip” (e.g., local minimum) in adouble-peaked signal, etc.

In accordance herewith, the measured ECGs are transformed intotwo-dimensional time-frequency maps by a suitable mathematicaltransform, such as, for instance, wavelet transform. For a givencontinuous ECG signal x(t), the continuous wavelet transform is givenby:

${{W\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{\overset{\_}{\psi \left( \frac{t - b}{a} \right)}{x(t)}{dt}}}}},$

where ψ is a selected wavelet, b corresponds to a shifted position intime and a to a scaling factor, and W(a,b) is the two-dimensionalfunction of position in time and scale resulting from the transform,also called wavelet coefficients. Similarly, for a discretized ECGsignal x (k) (where k is an integer), the continuous wavelet transformis given by:

${{W\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\sum\limits_{k}\; {{x(k)}\left( {{\int_{- \infty}^{{({k + 1})}T}{\overset{\_}{\psi \left( \frac{t - b}{a} \right)}{dt}}} - {\int_{- \infty}^{kT}{\overset{\_}{\psi \left( \frac{t - b}{a} \right)}{dt}}}} \right)}}}},$

where T is the sampling period. The wavelet selected for processing maybe, for example, a Mexican hat wavelet, Morlet wavelet, Meyer wavelet,Shannon wavelet, Spline wavelet, or other wavelet known to those ofordinary skill in the art. Other well-known time-frequency transformsthat may be used alternatively to continuous or discrete wavelettransform include, e.g., the short-term Fourier transform.

The time-frequency maps (such as, e.g., scalograms) generally includeboth positive and negative values, i.e., they are “signed.” In someembodiments, the absolute value of the signal value (or the square ofthe signal value) is taken at each time-frequency point, resulting in an“unsigned” time-frequency map. Analysis of the waveforms may be based onthe signed or unsigned time-frequency map or a combination of both,depending on the particular operation performed. For the purpose ofdisplaying the time-frequency map to a user (e.g., a physician), theunsigned version may be beneficial since it avoids presentingpotentially distracting information that is not of immediate,intuitively discernible clinical significance because the sign is notrelevant to an intuitive interpretation of the signal value of thetime-frequency map as a measure of the electrical energy of the heart.

FIGS. 3A and 3B illustrate an example ECG for a normal heart and anunsigned scalogram resulting from its wavelet transform (followed bytaking the absolute value), respectively. In the scalogram, the positionb corresponding to time is along the abscissa and the scale a(corresponding to frequency) along the ordinate, and the signal value Wis encoded by color or, as depicted, by gray-scale. (Alternatively, Wcould be represented as the height in a three-dimensional surface plotshown in perspective view.) As can be seen, the various peaks of thenormal ECG are reflected in relatively high intensity in the scalogram,allowing identification of the different ECG segments. For comparison,FIGS. 3C and 3D show an example ECG and associated scalogram,respectively, for an abnormal heart. Here, features that are prominentin the normal scalogram (e.g., the T wave) have rather low intensity.While this lower intensity generally tracks the lower values of the Twave in the ECG, it will be appreciated that the scalogram may providebetter visual clues. Accordingly, the scalogram can aid a physician orother skilled clinician to assess heart functioning.

To facilitate meaningful comparisons between time-frequency maps derivedfrom ECGs obtained simultaneously for different leads, thetime-frequency maps may be normalized. Normalization may involve scalingand/or shifting signal values in the time-frequency map to map the rangeof signal values in the map (or at least a portion of the map) to aspecified numerical range (hereinafter “target range”), e.g., 0 to 255or −128 to +127 (as are convenient ranges for binary representations,and can, in turn, be straightforwardly mapped onto color or gray-scalevalues for display). Using a particular normalization and the associatedtarget range consistently not only across leads, but also acrossmeasurements taken at different times and/or even for different patientsmay also serve to improve comparability of data over time and across thepatient population, as it eliminates or at least reduces overallsignal-level variations, which are often not attributable to differentheart conditions, allowing physicians to focus on the clinicallyrelevant relative signal levels within a time-frequency map.

The normalization may be based on a regional maximum and minimum definedas the maximum and minimum of the time-frequency map across frequencyand across time within a selected interval, and may then be applied to asecond selected interval that may or may not be the same as the firstselected interval. The maximum and minimum of the time-frequency mapacross frequency and across time within that second selected intervalare hereinafter called the absolute maximum and minimum, and they may,but need not, coincide with the regional maximum and minimum. The firstselected interval is typically, but not required to be, shorter than thesecond selected interval. In some embodiments, the regional maximum andminimum are determined across the entire time-frequency map,corresponding to the entire measurement time of the ECG from which it isderived, and the normalization is applied over that same range (suchthat the first and second selected intervals are equal). In otherembodiments, the regional maximum and minimum are identified within aportion of the time-frequency map that is limited in its time dimension,e.g., to an integer number of heartbeats (e.g., disregarding partialheartbeats) or only a single heartbeat. A time-frequency mapencompassing multiple heartbeats may, for instance, be broken up intoportions corresponding to individual heartbeats, and each portion may benormalized separately (potentially resulting in some discontinuity ofthe signal values in the normalized time-frequency map); in this case,first and second selected intervals are likewise equal to each other.Normalization may even be based on a time interval encompassing onlypart of a heartbeat, selected to likely (but not certainly) include theabsolute maximum and minimum. For instance, in some embodiments,regional maximum and minimum are determined within a portion of atime-frequency map that encompasses at least the RS segment. Note,however, that it is possible for, e.g., the T wave maximum to exceed themaximum in the QRS complex. In cases where the absolute maximum andminimum of the time-frequency map lie outside the portion of the mapacross which the regional maximum and minimum are determined, thenormalization will result in signal values exceeding the target range.(Normalization may also be applied in the time domain. In this case, theregional minimum and maximum are across time over the selected timeinterval.)

Normalization may be applied according to the following equation:

${n = {{\left( {d - d_{\min}} \right)*\frac{\left( {n_{\max} - n_{\min}} \right)}{\left( {d_{\max} - d_{\min}} \right)}} + n_{\min}}},$

where

n is the normalized data point;

n_(min) is the normalized target-range minimum;

n_(max) is the normalized target-range maximum;

d is the data point to be normalized;

d_(min) is the regional minimum; and

d_(max) is the regional maximum.

For example, to map onto the target range from 0 to 255, n_(min) is 0and n_(max) is 255; in effect, this normalization shifts thetime-frequency map to a minimum equal to zero and thereafter scales theshifted map based on its shifted regional maximum. More generally, thenormalization shifts the time-frequency map to a minimum equal ton_(min) and then scales the values of the shifted time-frequency map(taken relative to the minimum value) by the ratio of the differencebetween maximum and minimum of the target range to the differencebetween the regional maximum and minimum.

Normalization can be applied to signed as well as unsignedtime-frequency maps. As will be appreciated, the result of thenormalization will vary depending on whether the underlyingtime-frequency map is signed or unsigned. For example, when mapping asigned time-frequency map with a positive R peak and a negative S peakonto the target range from 0 to 255, several of the frequencies at thepoint in time corresponding to the S peak will map to or near zero.However, when the normalization is applied to the absolute value of theotherwise same time-frequency map, some frequencies at points in timebetween R and S will now map to or near zero whereas several of thefrequencies at the point in time corresponding to the S peak will maponto a relatively larger positive number within the target range.

FIG. 4 is a flow chart illustrating multiple variants of a method 400for the time-frequency analysis of electrocardiograms in accordance withvarious embodiments. Starting point of the method 400 is the measurementof one or more ECGs associated with one or more respective leads (action402), using one or more electrodes placed on a patient. In someembodiments, ten electrodes are used to obtain twelve leads. (The phrase“measuring electrocardiograms” is intended to encompass both theacquisition of electrocardiogram signals with the electrodes, and thedigitization and/or initial processing of these signals to generate anelectrocardiogram for each lead, which may include combining multipleelectrocardiogram signals to obtain an electrocardiogram for a singlelead, as described above.) In action 404, the ECG(s) are converted bytime-frequency transform (e.g., wavelet transform) into one or morerespective two-dimensional time-frequency maps (e.g., scalograms),using, e.g., the time-frequency-transform module 112 depicted in FIG. 1.The time-frequency map(s) may be used in the original signed form, orconverted to unsigned map(s) by taking the absolute value at eachtime-frequency point, or both. Further, the time-frequency map(s) may benormalized, as described above. In some embodiments, for example, thetime-frequency map is normalized based on the maximum and minimumidentified in the time-frequency map across frequency and across a timeinterval encompassing at least the RS segment (and, in some embodiments,encompassing a full cardiac cycle (or heartbeat), multiple (an integernumber of) cardiac cycles, or the entirety of the measurement time).

The time-frequency map(s) may be analyzed (wholly or partiallyautomatically, e.g., using the analysis module 116) to characterize,qualitatively and/or quantitatively, one or more types of waveforms ofinterest, depending, e.g., on the specific diagnostic purpose. Forexample, the T wave may be analyzed to assess the health and functioningof the ventricles, and the P wave may be analyzed to assess the healthand functioning of the atria. For a given type of waveform, thatwaveform, and the time range in which it occurs, is identified in thetime-frequency map (action 406), either directly, or indirectly by firstidentifying the waveform in the corresponding ECG and then applying theassociated time range to the analysis of the time-frequency map. In someembodiments, waveforms and features are ascertained based on theirtemporal relation to another feature. For example, the R peak may beused as a reference point, and high-intensity regions preceding andfollowing the R peak by a certain amount of time (within specifiedmargins, and generally depending on the heart rate) may then beidentified as the P wave or T wave, respectively. The onset of the Twave, for instance, is known to be typically not earlier than about 120ms after the R wave. Thus, viewing the ECG or time-frequency map in itsentirety, it is usually possible to correlate the various waveformsoccurring in the signals with known phases within the cardiac cycle. Inembodiments that utilize ECGs and time-frequency maps for multipleleads, it may be beneficial to apply a single time range determined fora waveform consistently across data for all leads. The lead selected fordetermining that time range may be, e.g., the lead whose respective ECGexhibits the largest difference between the maximum and minimum valuefor the waveform. Alternatively, the lead with the greatestmaximum-to-minimum difference for the selected waveform may serve todetermine the point in time at which the waveform peaks for that lead,and that point in time may then be used as a starting point to locatethe waveform and its peak in the ECGs or time-frequency maps for otherleads. While these methods are amenable to straightforward automation,it is also possible, in alternative embodiments, to utilize human inputin waveform identification. For example, a user interface may displaythe ECG and time-frequency transform in temporal alignment, and allow auser to identify waveforms, e.g., by clicking on or near waveform peaksand/or adjusting visual delimiters with a mouse or other cursor-controldevice.

In various embodiments, once the time range associated with a selectedwaveform has been determined, the time-frequency map may be analyzedqualitatively to determine the general waveform morphology (action 408).Morphology determination may involve, for instance, distinguishingbetween monophasic waves (i.e., waves whose signal values are either allpositive or all negative), biphasic waves (i.e., waves whose signalvalues transition along the wave from positive to negative or viceversa, thus forming a positive “phase” and a negative “phase” of thewave), and, optionally, triphasic waves (i.e., waves whose signal valuestransition twice between positive and negative, forming three “phases”).For monophasic waves, the analysis may further include determiningwhether the peak is positive or negative, and whether it is a singlepeak (i.e., a single local extremum) or double peak (i.e., a pair oflocal extrema separated by a small “dip”). For multiphasic waves (e.g.,biphasic or triphasic waves), the analysis may include determining inwhich direction the signal value transitions (e.g., positive to negativeor vice versa). In general, some determinations of the overallmorphology can be made based on an unsigned time-frequency map whereasothers may use the signed map. FIG. 5, discussed below, illustrates anexample process for morphology determination. The determined morphologymay inform the subsequent quantitative waveform analysis. In the absenceof this optional step, the waveform is treated as monophasic by default.

Quantitative measures characterizing a waveform, such as one or morelocal extrema of the waveform and/or their attributes (e.g., anamplitude measure or the frequency where the extremum occurs) can bedefined and determined in various ways, reflected in three partiallyoverlapping prongs in FIG. 4. The number of extrema to be identified maygenerally depend on the waveform morphology. For instance, for amonophasic waveform that has not been determined to be double-peaked, asingle extremum is identified, whereas for a biphasic waveform, twoextrema (two maxima in an unsigned time-frequency map, and a maximum andminimum in a signed time-frequency map) are determined.

In some embodiments (reflected in the left prong), a portion of thetime-frequency map corresponding to the time range determined for theselected waveform (or forming a substantial sub-range thereof) issearched in both time and frequency for one or more extrema (action410). In other embodiments (reflected in the right prong), one or morepoints in time are first determined in the ECG (action 412), and thetime-frequency map is then analyzed at these points in time (or innarrow bands surrounding these points in time) to determine one or moreextrema across frequency only (or substantially across frequency in thecase of narrow time bands) (action 414). For example, early and latetimes bracketing the peak of a wave may be identified in the ECG (e.g.,based on a set amplitude as a fraction of peak amplitude, or based on aset time interval relative to the peak time) to determine early and latemeasures corresponding to the extrema across frequency at these points(or within these bands) in time. As another example, the time at whichthe waveform peaks in the ECG may be used to determine a peak measurecorresponding to the extremum across frequency at the point (or within aband) in time where the waveform peaks. (Herein, the phrase “one or morepoints in time” is intended to include the case of time bands, which maybe used instead of discrete points in time, e.g., to reduce the effectof noise and/or increase the robustness of the extrema determinations.)

In yet other embodiments (reflected in the center prong), the points intime at which the time-frequency map is to be identified are themselvesdetermined from the time-frequency map by analyzing the map across timeat a selected frequency or at multiple selected frequencies (or withinnarrow bands surrounding the selected frequencies), or across time andfrequency within one or more selected frequency ranges (e.g., alow-frequency range, a high-frequency range, or a mid-frequency range)(action 416). The frequency/frequencies or frequency range(s) may beselected, e.g., based on the waveform itself and/or the waveformmorphology (action 418). In general, since the inverse of the frequency(corresponding to the scale in a scalogram) can be taken as an estimateof the width of a waveform, higher frequencies may be used for narrowerwaveforms and waveform features while lower frequencies may be used forwider waveforms and waveform features. For example, monophasic T wavesare generally wider than monophasic P waves, so a lower frequency orfrequency range (corresponding to higher scale) is used for monophasic Twaves. The frequencies used for biphasic waves (whose two “humps” areusually each about half the width of a monophasic wave of the same type)are generally higher than the frequencies used for their monophasiccounterparts. Double-peaked and triphasic waveforms typically use evenhigher frequencies (corresponding to narrower widths of the individualhumps). In the case of a double-peaked waveform, first the generalenergy (e.g., as may be reflected in the maximum amplitude of thewaveform in the time-frequency map), center location, and/or overallwidth may be determined at a relatively lower frequency, and second, theenergy, location, and/or width of the two humps may be determined at arelatively higher frequency.

Various measures can be defined based on attributes of the identifiedextrema across time and/or frequency. Such measures include, forexample, amplitude measures, such as the amplitude (i.e., signal value)of the extremum or an average or integral of the signal values over asmall region encompassing the extremum (e.g., a region defined by signalvalues exceeding a specified fraction of the amplitude at the extremum),frequency measures associated with the extrema (which correspond to theinverse of the widths of the respective waveforms in the time domain),or time measures associated with the extrema (e.g., the interval betweenthe locations in time of the maxima of the phases). These and/or similarmeasures may be compared against each other and/or against thresholdsbased on known correlations with normal and abnormal heart conditions(action 420) to ultimately compute indices indicative of heart conditionand/or detect various specific disease conditions (action 422). Theindices or disease conditions may then be communicated to a user (action424), e.g., via display in a user interface on screen, in a printedreport, or by email. Examples of various applications are providedbelow.

Referring now to FIG. 5, an example method 500 for determining themorphology of a waveform is illustrated. As shown, the morphologyanalysis may begin by determining from the unsigned time-frequency mapwhether a waveform is monophasic or biphasic (at 502). For a biphasicwave, the region for the waveform is separated by a band of low (zero orclose-to-zero) values (generally depicted as a dark band) in tworegions. Similarly, for a triphasic wave, the band would be separatedinto three distinct regions. By contrast, a monophasic wave appears as asingle region without dark bands. If the waveform is determined to bemonophasic, the unsigned time-frequency map may be further analyzed todetermine whether the waveform is single-peaked or double-peaked (at504). A double peak will be reflected in two extrema, separated by aregion of lower, non-zero values. For a biphasic wave, the analysis mayproceed to determining, from the signed time-frequency map, whether thephase transitions from positive to negative values or vice versa (at506). Similarly, the sign of a monophasic wave may be determined fromthe signed time-frequency map (at 508). A positive phase in thetime-frequency map corresponds to a positive-going waveform in the ECG,that is, the waveform peaks in the positive direction (i.e., firstincreases and then decreases in value), and a negative phase in thetime-frequency map corresponds to a negative-going waveform in the ECG,that is, the waveform peaks in the negative direction (i.e., firstdecreases and then increases in value). (Note that “positive-going” and“negative-going” do not imply that all values of the waveform in the ECGare positive or negative, respectively, as the ECG may have an overallbaseline that off-sets all values by a positive or negative amount,which may result, e.g., in a positive-going wave having negativevalues.) As will be appreciated, the determination of a waveform asmonophasic or biphasic, and subsequent determinations of the signsassociated with the peaks, may also be determined wholly based on thesigned time-frequency map. In a color-coded map where blue representsnegative values and red represents positive values (with green or yellowcorresponding to the demarcation between positive and negative), forinstance, all-red waves will be identified as monophasic positive,all-blue waves as monophasic negative, and waves transitioning from blueto red (or vice versa) as biphasic.

Of course, the degree to which waveform morphology is analyzed maydiffer between embodiments. In the absence of any morphologydetermination, the waveform is implicitly treated as monophasic. A basicmorphology analysis may simply discriminate between monophasic andbiphasic waveforms, whereas a more detailed analysis may distinguishbetween monophasic, biphasic, and double-peaked waveforms. An even moresophisticated analysis may provide determinations whether a waveform ismonophasic (positive or negative), biphasic (positive-negative ornegative-positive), double-peaked (positive or negative), or triphasic(positive-negative-positive or negative-positive-negative).

To illustrate different waveform morphologies in the time-frequencydomain, FIGS. 6A-6E provide example ECGs and corresponding normalized,unsigned scalograms taken for a diseased patient for leads II, aVL, V1,V2, and V4, respectively. As can be seen by comparison across leads, agiven waveform may assume different morphologies in different leads.

For example, with reference to FIGS. 6A and 6B, the T wave (generallyindicated at 600 in the ECG of FIG. 6A, and present at the samelocation, but hardly visible in the ECG of FIG. 6B) is, in this example,monophasic in lead II (FIG. 6A) and biphasic in lead aVL (FIG. 6B). Themonophasic wave in lead II is reflected in a bright region 601 in FIG.6A. For lead aVL, the amplitude of the T-wave is lower, but the biphasicnature of the wave is clearly evident, in FIG. 6B, in the two lighterregions 602, 603, which correspond to the two phases within the biphasicwave. The cross-over point of the two phases is indicated by theseparation between these two regions, and the location and amplitude ofthe two phases can be determined by the extrema in these two regions. Ifthe type of biphasic wave (positive-negative or negative-positive) isdifficult to determine from the low-level ECG amplitude, which may beoff-baseline, the type can be determined from the non-normalized signedscalogram.

With reference to FIG. 6E, in lead V4, the T wave appears double-peaked.To find the central location of the low-level generally positive-goingT-wave (which, again, is barely visible in the ECG), the gradient peakalong the mid-frequencies can be used. While not always the case, theposition in time for this example waveform usually corresponds to thedip 605 at the top of the scalogram frequency range, indicating thevalley between the two peaks. The two (barely visible) ECG peaks of theT wave correspond to the two lighter peaks at the top of the scalogram.The position in time of these two peaks and the valley between them mayserve as the points in time at which extrema across frequency aremeasured. While these points may be determinable from the ECG, use ofthe scalogram to determine these points is beneficial because the localextrema are a function of both time and frequency.

With reference to FIGS. 6A, 6C, and 6D, the P wave (indicated in the ECGof FIG. 6A at 606, and in the scalograms at 607, 608, 610) appearsmonophasic in V2 (FIG. 6D), biphasic in lead V1 (FIG. 6C), anddouble-peaked in lead II (FIG. 6A). With a double peak in lead II and alarger negative phase in V1, the patient shows signs of left atrialenlargement (discussed in more detail further below). Note that thescalogram assists in showing that the negative phase of the V1 biphasicP wave is double-peaked. By measuring the extrema at multiple points inthe waveform, the measurements can be compared and a threshold can beapplied to assist in determining disease state.

With reference again to FIG. 6D, showing lead V2, the local extrema(maxima) across time at a given (normalized unsigned) frequency (scale)can be used to select the Q, R, and S points in time for analysis of theQRS complex. In FIG. 6D, only the R (612) and S (614) points are shown.Note, these points in time may not be the traditionally definedlocations for the Q, R, or S waveforms. Rather, these locations aredesigned to extract features of the waveform that correlate withdisease. In turn, the extrema across the frequencies for the selectedpoints in time can be used as the measures upon which the indices arecalculated.

The above-described waveform analysis can be used to test for variousspecific disease conditions. For example, in one embodiment, a conditionknown as (right or left) atrial enlargement can be detected based oncertain characteristics of the P wave. Applying the method 400 of FIG. 4to the unsigned time-frequency map for lead V1, after having determinedthe P wave to be biphasic, the amplitude and frequency (being inverselyproportional to temporal width) of the extrema (that is, in the unsignedtime-frequency map, the maxima) of the two phases are determined.Relative to a normal biphasic P wave in V1, the maximum of the firstphase (that is, the phase occurring at earlier times) beingsubstantially larger in amplitude and being located at a substantiallyhigher frequency (corresponding to a first phase that is shorter intime), or the maximum of the second phase (that is, the phase occurringlater in time) being substantially smaller in amplitude and located at asubstantially higher frequency (corresponding to a second phase that isshorter in time), can indicate right atrial enlargement (RAE). Comparingthe two measured phases against each other, the first phase beingsubstantially larger in amplitude than the second phase would likewiseindicate RAE. Similarly, left atrial enlargement (LAE) can be indicatedby the first maximum of the first phase being smaller in amplitude orthe maximum of the second phase being larger in amplitude and at a lowerfrequency, relative to a normal biphasic P wave in V1, or by the firstphase being substantially smaller in amplitude than the second phase.Accordingly, comparisons against absolute thresholds (e.g.,statistically derived for normal condition) and/or comparisons betweenmeasurements can help determine the existence and/or the degree of RAEor LAE. A substantially larger amplitude and width of both the first andsecond phases may indicate the simultaneous existence of both RAE andLAE.

In the present context of comparisons between waveform extrema or otherfeatures for diagnosis of disease condition, an amplitude is deemed“substantially larger” (or “substantially smaller”) than the amplitudeit is compared against if it is larger (smaller) by at least a specifiedamount empirically determined to be (by itself or in conjunction withother conditions) abnormal, i.e., indicative of a disease condition orhigh likelihood of a disease condition. For example (and withoutlimitation), the amplitude of one of the maxima of a biphasic P wave maybe deemed substantially larger than the amplitude of the correspondingmaximum of a normal biphasic P wave if it exceeds the latter by at least20%, or at least 50%, or some other specified value. Similarly, afrequency associated with a maximum (or other waveform feature) isdeemed “substantially lower” (or “substantially higher”) than thefrequency it is compared against (such as the frequency of acorresponding feature in a normal waveform) if it deviates from thelatter frequency by a specified amount empirically determined to beabnormal.

When the method 400 is applied to lead II, it is first determinedwhether the P wave is monophasic or double-peaked. A double peak mayindicate RAE and/or LAE. RAE is indicated if the first peak issubstantially larger than the second peak. LAE is indicated if thesecond peak is substantially larger and wider than the first peak,and/or the summation of widths (as determined from the inverse of thefrequencies) is substantially larger than for the normal monophasicwaveform. Both RAE and LAE are indicated if the amplitudes of the phasesare similar, but the second phase is substantially wider than the firstand the summation of widths is substantially larger than for the normalmonophasic waveform. The preceding measurements and comparisons may befed into a set of rules to determine the likelihood of RAE and/or LAE.LAE is also an indicator of advanced diastolic dysfunction.

In another embodiment, the RS segment of the QRS complex is used to testfor (right or left) ventricular hypertrophy. For this purpose, the RSsegment may be treated as a biphasic waveform. Applying the method 400to lead V1, a normal diagnosis is indicated if both phases are similarin amplitude. A substantially larger first phase indicates potentialright ventricular hypertrophy (RVH), while a substantially larger secondphase indicates potential left ventricular hypertrophy (LVH). In leadV6, a normal diagnosis is indicated by the first phase being about twicethe amplitude of the second phase. If the second phase is twice theamplitude of the first phase, potential RVH is indicated. If the firstphase is greater than four times the amplitude of the second phase,potential LVH is indicated. Similar thresholding can be applied to leadsV2-V5. Suitable lead-dependent thresholds may be determined bystatistical evaluation of a database of labeled patients. Themeasurements and comparisons may be fed into a set of rules to determinethe likelihood of RVH and/or LVH.

In yet another embodiment, the T wave morphology in leads V2 and V3 isanalyzed to diagnose disease. In V2 and V3, the T wave is typicallypositive monophasic and sometimes low-amplitude biphasic.Positive-negative biphasic T waves, known as Type 2 Wellen's syndrome,are specific for critical stenosis of the left anterior descendingartery. Negative-positive biphasic T waves in leads V2-V3 can indicatehypokalaemia.

In an ECG, signal portions associated with the T wave represent therepolarization of the ventricles. The signal portions associated withthe P wave, on the other hand, reflect atrial depolarization. In certainembodiments, therefore, measurements are taken at various pointsassociated with the T wave or P wave to check for proper ventricularrepolarization and atrial depolarization. For example, extrema acrossfrequency may be determined at points in time (or within narrow timebands) where the T wave or P wave peaks (i.e., assumes its maximum) inthe ECG, and/or for early and/or late points in time (or narrow timebands) preceding and/or following the maximum of the T wave or P waveand being in the vicinity of that maximum (e.g., points falling within atime interval defined by two points bracketing the T-wave or P-wavemaximum at which the T wave or P wave, respectively, assumes half itsmaximum value). The amplitudes of the extrema (or amplitude measurescorresponding to integrals or averages over the immediate neighborhoodof the extrema) associated with the T wave are herein referred to as“repolarization measures” (e.g., repolarization peak, early, and latemeasures, respectively), and the amplitudes (or similar amplitudemeasures) of the extrema associated with the P wave are referred to as“depolarization measures” (e.g., depolarization peak, early, and latemeasures, respectively). From the repolarization and depolarizationmeasures, one or more repolarization or depolarization indices may bederived, e.g., by averaging, dividing by the heart rate, and/or makingadjustments based on information external to the ECG or time-frequencymap (including, e.g., age- and/or gender-dependent adjustment factors).For example, if the repolarization/depolarization measures are obtainedbased on ECGs covering multiple cardiac cycles, the individuallydetermined maxima may be averaged over these cycles. Further, thevarious repolarization/depolarization measures can generally be derivedseparately from different time-frequency maps obtained for differentrespective leads, and repolarization/depolarization measures of the sametemporal type (e.g., the repolarization early measures) may be averagedacross multiple leads. In particular, repolarization or depolarizationindices specific to the left and right ventricles or atria,respectively, may be derived by averaging only across leads associatedwith the same (i.e., left or right) ventricle or atrium. For example,ventricular indices for the right ventricle and atrial indices for theright atrium may be calculated by (e.g., arithmetically) averaging overthe repolarization measures of leads V1 and V2, and ventricular indicesfor the left ventricle and atrial indices for the left atrium may becalculated by averaging over the repolarization measures of leads V4,V5, and V6. In some embodiments, repolarization/depolarization measuresor repolarization/depolarization indices are compared against eachother, or against a threshold, to assess whether, and/or to whichdegree, heart function is impaired. For example, a laterepolarization/depolarization measure exceeding an earlyrepolarization/depolarization measure, a right ventricularrepolarization index exceeding a left ventricular repolarization index,a right atrial depolarization index exceeding a left atrialdepolarization index, or an early or late repolarization/depolarizationmeasure falling below a specified threshold may all indicate anabnormality in heart function. An index for the heart as a whole may becomputed from respective indices for the left and right ventricles orthe left and right atria, e.g., by forming the ratio, difference, orsome other function of left and right ventricular or atrial indices.

While some of the above examples of diagnostic applications are based onwaveform morphologies that are, in principle, also detectable intime-domain ECG signals, taking measurements in the time-frequencydomain, especially if the time-frequency map is a scalogram obtained bycontinuous wavelet transform, has multiple advantages. For instance, ina scalogram, amplitude, width (inverse-frequency), and time measurementscan be made independently of determining a baseline position. Inaddition to minimizing the effect of residual baseline wander noise,removing the average from a generally stable recorded ECG does notensure a zero baseline due to, for example, a large amplitude R waveand/or T wave. Measurements made by the disclosed method are notaffected by any otherwise required baseline position estimation.

Further, either noise or fluctuations in the actual heart signal canmask desired information or create variability in measurements takenusing the traditional time-domain ECG. The time-frequency map (andspecifically the scalogram) can group energy in time and frequency toprovide a measurement that is not affected by fluctuations outside thetime-frequency region of interest. To further mitigate undesired signalfluctuations, the time-frequency map can be smoothed not only in time(aka filtering), but also in frequency. This is beneficial, and in somecases necessary, to facilitate the proper operation of gradientalgorithms that find localized extrema across both time and frequency.

FIG. 7 shows an example heart test device 700 in perspective view. Thedepicted device takes the form of a tablet computer 700 including atouchscreen display 702 as well as a control panel 704 with physicalbuttons (e.g., to power the tablet 700 on/off). In some embodiments, asshown in FIG. 8, the display 702 presents a multi-tab user interfaceincluding, e.g., home, patient, test, report, and settings screens. Someof the tabs (shown along the right edge of the display 702) may beduplicated by the physical buttons of the control panel 704, allowing anoperator to navigate between different screens and associated devicefunctions in different ways. Electrodes for acquiring the ECG signalsmay be hooked up to the tablet computer 700 via a suitable connector 706(e.g., a DB15 connector). The tablet 700 contains a general-purposeprocessor and volatile as well as non-volatile memory storinginstructions for implementing the functional processing modules 110,112, 116, 118. Of course, in various alternative embodiments, the hearttest device may take different form factors, such as that of a desktopcomputer, laptop computer, or smartphone (to name just a few), each witha suitable electrode interface, which may include custom circuitry forconverting the electrode signals into digital signals suitable forfurther processing with software. Furthermore, an electrocardiographysystem providing the functionality described herein need not necessarilybe implemented in a single device, but can be provided by multipledevices used in combination, e.g., a conventional ECG monitor connectedto a general-purpose computer running software to implement theprocessing functionality described herein.

Turning now to the user interface, FIG. 8 depicts an example reportscreen (as may appear within the display 702) in accordance with variousembodiments. As shown, the report screen may be partitioned intomultiple screen portions arranged in an intuitive manner so as to allowthe viewer to quickly locate the desired information. At the top of thescreen, patient information, such as a unique patient identifier and thepatient's name, as well as patient-specific parameters affecting theinterpretation of the ECGs, such as age and gender, may be displayed,along with a record identifier (not shown) composed of, e.g., a date andtime stamp for the test. In a left panel, ECGs and time-frequency mapsfor one or more leads (e.g., as depicted, leads I, II, and III) may bedisplayed, e.g., in a vertical arrangement. The time-frequency maps canvisualize information not discernible from the ECGs from which they arederived, e.g., by providing a picture of the electrical energy of theheart during various stages within the cardiac cycle, and can be usefulin detecting various disease conditions, including conditions nottraditionally diagnosed based on ECGs, such as, e.g., myocardialischemia. ECGs are included in the display because of their familiarityto physicians and other medical practitioners and for the purpose ofidentifying temporally defined features of the signal, such as the QRScomplex, P wave, and T wave. In accordance with various embodiments, thesignal value of the time-frequency map (e.g., the electrical potentialor voltage that is plotted as a function of time and frequency) isencoded in a color scale (reflected, in FIG. 8, in different shades ofgray corresponding to the different brightness values of variouscolors). While the signal value itself, as resulting from thetime-frequency (e.g., short-time Fourier or wavelet) transform appliedto the ECG, may be a signed value (generally resulting in both positiveand negative values across the map), the color-coded depicted value maybe unsigned, as obtained from a signed value by computing the absolutevalue. Using unsigned signal values in the color-coded maps serves torepresent the energy level of the time-dependent frequency content,independent of the phase of those frequencies, thus allowing the energyof either positive or negative phase to appear at the same point (alongfrequency) on the time-frequency map.

As described above, the ECGs and time-frequency maps may be analyzed, inaccordance with various embodiments, to provide quantitative indicesindicative of heart health and/or a qualitative assessment orcategorization. The results of the analysis may be presented, as shownin the right panel of FIG. 7, in numerical, textual, and/or graphicform. For example, as shown, the right panel may include an “energyicon” representing the patient's overall heart health, a number ofnumerical indices (e.g., repolarization or depolarization indices asdescribed above) providing a more detailed picture underneath the icon,and a conventional Glasglow-analysis textual summary underneath thenumerical indices. The Glasgow-analysis summary portion may display suchmetrics, derived from the ECGs, as the patient's heart rate anddurations of certain ECG features (such as the QRS complex). Inaddition, it may summarize the quality and reliability of the test,e.g., based on signal-to-noise levels of various leads. Glasgow analysisis known to those of ordinary skill in the art, and will not be furtherelaborated upon herein.

Certain embodiments are described herein as including a number of logiccomponents or modules. Modules may constitute either software modules(e.g., code embodied on a non-transitory machine-readable medium or in asignal transmitted over a network) or hardware-implemented modules. Ahardware-implemented module is a tangible unit capable of performingcertain operations and may be configured or arranged in a certainmanner. In example embodiments, one or more computer systems (e.g., astandalone, client or server computer system) or one or more processorsmay be configured by software (e.g., an application or applicationportion) as a hardware-implemented module that operates to performcertain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware-implemented module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor configured using software,the general-purpose processor may be configured as respective differenthardware-implemented modules at different times. Software mayaccordingly configure a processor, for example, to constitute aparticular hardware-implemented module at one instance of time and toconstitute a different hardware-implemented module at a differentinstance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses) thatconnect the hardware-implemented modules. In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or processors or processor-implementedmodules. The performance of certain of the operations may be distributedamong the one or more processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processor or processors may be located in a singlelocation (e.g., within a home environment, an office environment or as aserver farm), while in other embodiments the processors may bedistributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., Application Program Interfaces (APIs).)

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor,a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice.

FIG. 9 is a block diagram of a machine in the example form of a computersystem 900 within which instructions for causing the machine to performany one or more of the methodologies discussed herein may be executed.In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Whileonly a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

The example computer system 900 includes one or more processors 902(e.g., a central processing unit (CPU), a graphics processing unit (GPU)or both), a main memory 904 and a static memory 906, which communicatewith each other via a bus 908. The computer system 900 may furtherinclude a video display unit 910 (e.g., a liquid crystal display (LCD)or a cathode ray tube (CRT)). The computer system 900 also includes analphanumeric input device 912 (e.g., a keyboard), a user interface (UI)navigation device 914 (e.g., a mouse), a disk drive unit 916, a signalgeneration device 918 (e.g., a speaker), a network interface device 920,and a data interface device 928 (such as, e.g., an electrode interface106).

The disk drive unit 916 includes a machine-readable medium 922 storingone or more sets of instructions and data structures (e.g., software)924 embodying or utilized by any one or more of the methodologies orfunctions described herein. The instructions 924 may also reside,completely or at least partially, within the main memory 904 and/orwithin the processor 902 during execution thereof by the computer system900, the main memory 904 and the processor 902 also constitutingmachine-readable media.

While the machine-readable medium 922 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions or data structures. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding, or carrying instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention, or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., Erasable Programmable Read-Only Memory (EPROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; CD-ROM and DVD-ROM disks, or otherdata-storage devices. Further, the term “machine-readable medium” shallbe taken to include a non-tangible signal or transmission medium,including an electrical signal, a magnetic signal, an electromagneticsignal, an acoustic signal and an optical signal.

The following numbered examples are illustrative embodiments:

1. A method comprising, using one or more hardware processors:converting one or more electrocardiograms associated with one or morerespective leads by time-frequency transform into one or more respectivetwo-dimensional time-frequency maps; and, for a selected type ofwaveform, identifying the waveform of the selected type within the oneor more time-frequency maps, determining one or more morphologies of theidentified waveform within the one or more time-frequency maps,analyzing the one or more time-frequency maps based at least in part onthe one or more morphologies of the identified waveform to determine oneor more extrema associated with the identified waveform within the oneor more time-frequency maps, and determining a disease condition basedon at least one of one or more amplitude measures, one or more frequencymeasures, or one or more time measures of the determined one or moreextrema.

2. The method of example 1, wherein analyzing the one or moretime-frequency maps comprises analyzing at least one of the one or moretime-frequency maps across time at one or more frequencies or within oneor more frequency ranges selected based at least in part on a respectiveat least one morphology of the waveform to determine one or more pointsin time associated with the selected waveform, and determining the oneor more extrema across frequency at the determined one or more in time.

3. The method of example 1, wherein analyzing the one or moretime-frequency maps comprises determining the one or more extrema acrosstime and frequency in the one or more time-frequency maps within a timeinterval associated with the waveform.

4. The method of example 1, wherein analyzing the one or moretime-frequency maps comprises determining, from the respective one ormore electrocardiograms, one or more points in time associated with thewaveform, and determining the one or more extrema across frequency atthe determined one or more points in time.

5. The method of any one of examples 1-4, wherein one or more numbers ofextrema determined in the one or more time-frequency maps depend on therespective one or more morphologies of the waveform.

6. The method of any one of examples 1-5, wherein determining thedisease condition comprises comparing at least one of the one or moreamplitude measures, one or more frequency measures, or one or more timemeasures of the one or more extrema against one or more respectiveempirical thresholds and basing the determination of the diseasecondition on the comparison.

7. The method of example 6, wherein determining the disease conditioncomprises detecting atrial enlargement based at least in part on atleast one of: comparisons of the amplitude measures and frequencies ofextrema of a biphasic P wave in lead V1 against respective empiricalamplitude and frequency thresholds associated with a normal biphasic Pwave in lead V1; or comparison of the sum of inverse frequencies of adouble-peaked monophasic P wave in lead II against an empiricalthreshold associated with a normal monophasic P wave in lead II.

8. The method of any one of examples 1-5, wherein the disease conditionis determined based at least in part on a time-frequency map for whichextrema are determined at at least two points in time, and whereindetermining the disease condition comprises comparing the extrema at theat least two points in time against each other and basing thedetermination of the disease condition on the comparison.

9. The method of example 8, wherein determining the disease conditioncomprises detecting atrial enlargement based at least in part on atleast one of: comparisons of amplitude measures between extrema of abiphasic P wave in lead V1; or comparisons of amplitude measures betweenextrema of a double-peaked monophasic P wave in lead II.

10. The method of example 8, wherein determining the disease conditioncomprises detecting ventricular hypertrophy based on lead-dependentcomparisons of amplitude measures between extrema associated with abiphasic RS segment in any of leads V1, V2, V3, V4, V5, or V6.

11. The method of any one of examples 1-10, further comprisingnormalizing each of the one or more time-frequency maps based at leastin part on a difference between a maximum and a minimum identified inthe respective time-frequency map across time in an intervalencompassing an RS segment and across frequency, wherein the extrema aredetermined from the normalized time-frequency maps.

12. The method of any one of examples 1-11, further comprisingcommunicating the determined disease condition to a user.

13. One or more machine-readable media storing instructions forexecution by one or more processors, the instructions causing the one ormore processors to perform operations comprising: converting one or moreelectrocardiograms associated with one or more respective leads bytime-frequency transform into one or more respective two-dimensionaltime-frequency maps; and for a selected type of waveform, identifyingthe waveform of the selected type within the one or more time-frequencymaps, determining one or more morphologies of the identified waveformwithin the one or more time-frequency maps, analyzing the one or moretime-frequency maps based at least in part on the one or moremorphologies of the identified waveform to determine one or more extremaassociated with the identified waveform within the one or moretime-frequency maps, and, determining a disease condition based on atleast one of one or more amplitude measures, one or more frequencymeasures, or one or more time measures of the determined one or moreextrema.

14. The one or more machine-readable media of example 13, whereinanalyzing the one or more time-frequency maps comprises analyzing atleast one of the one or more time-frequency maps across time at one ormore frequencies or within one or more frequency ranges selected basedat least in part on a respective at least one morphology of the waveformto determine one or more points in time associated with the selectedwaveform, and determining the one or more extrema across frequency atthe determined one or more in time.

15. The one or more machine-readable media of example 13, whereinanalyzing the one or more time-frequency maps comprises determining,from the respective one or more electrocardiograms, one or more pointsin time associated with the waveform, and determining the one or moreextrema across frequency at the determined one or more points in time.

16. The one or more machine-readable media of example 13, whereinanalyzing the one or more time-frequency maps comprises determining theone or more extrema across time and frequency in the one or moretime-frequency maps within a time interval associated with the waveform.

17. The one or more machine-readable media of any one of examples 13-16,wherein one or more numbers of extrema determined in the one or moretime-frequency maps depend on the respective one or more morphologies ofthe waveform.

18. The one or more machine-readable media of any one of examples 13-17,wherein determining the disease condition comprises comparing at leastone of the one or more amplitude measures, one or more frequencymeasures, or one or more time measures of the one or more extremaagainst one or more respective empirical thresholds and basing thedetermination of the disease condition on the comparison.

19. The one or more machine-readable media of examples 13-17, whereinthe disease condition is determined based at least in part on atime-frequency map for which extrema are determined at at least twopoints in time, and wherein determining the disease condition comprisescomparing the extrema at the at least two points in time against eachother and basing the determination of the disease condition on thecomparison.

20. A device comprising: an electrode interface configured to receiveone or more electrocardiogram signals via one or more respectiveelectrodes operatively connected to the electrode interface; and aprocessing facility communicatively coupled to the electrode interfaceand configured to: generate one or more electrocardiograms for one ormore respective leads from the one or more electrocardiogram signals;convert the one or more electrocardiograms by time-frequency transforminto one or more respective two-dimensional time-frequency maps; and,for a selected type of waveform, identify the waveform of the selectedtype within the one or more time-frequency maps, determine one or moremorphologies of the identified waveform within the one or moretime-frequency maps, analyze the one or more time-frequency maps basedat least in part on the one or more morphologies of the identifiedwaveform to determine one or more extrema associated with the identifiedwaveform within the one or more time-frequency maps, and determine adisease condition based on at least one of one or more amplitudemeasures, one or more frequency measures, or one or more time measuresof the determined one or more extrema.

21. The device of example 20, further comprising a display operativelydisplaying the determined disease condition to the user.

22. The device of example 20, wherein the processing facility is furtherconfigured to perform any of the operations of examples 2-12.

23. A method comprising, using one or more hardware processors:converting one or more electrocardiograms for one or more respectiveleads by time-frequency transform into one or more respectivetwo-dimensional time-frequency maps; identifying, within the one or moreelectrocardiograms, one or more points in time associated with a P wave;determining, for at least one of the one or more time-frequency maps,one or more atrial depolarization measures corresponding to extremaacross frequency of the respective time-frequency map at the one or morepoints in time associated with the P wave; and outputting at least oneatrial depolarization index based on the one or more atrialdepolarization measures.

24. The method of example 23, further comprising normalizing each of theone or more time-frequency maps based at least in part on a differencebetween a maximum and a minimum identified in the respectivetime-frequency map across time in an interval encompassing an RS segmentand across frequency, wherein the one or more atrial depolarizationmeasures are determined from the normalized time-frequency maps.

25. The method of example 24, wherein the time interval across which themaximum and minimum are identified in the time-frequency map encompassesat least one heartbeat.

26. The method of example 24, wherein the time interval across which themaximum and minimum are identified in the time-frequency map encompassesa measurement time of the associated electrocardiogram in its entirety.

27. The method of example 24, wherein the time interval across which themaximum and minimum are identified in the time-frequency map correspondsto an integer number of heartbeats.

28. The method of any one of examples 23-27, wherein the one or morepoints in time associated with the P wave fall within a time intervaldefined by points preceding and following a maximum of the P wave atwhich the P wave assumes half of its maximum value.

29. The method of any one of claims 23-27, wherein the one or morepoints in time associated with the P wave comprise a first point in timepreceding the maximum of the P wave and a second point in time followingthe maximum of the P wave.

30. The method of any one of examples 23-29, further comprisingcomparing a first atrial depolarization measure corresponding to anextremum at the first point in time with a second atrial depolarizationmeasure corresponding to an extremum at the second point in time.

31. The method of example 30, further comprising determining a heartcondition based on the comparison.

32. The method of example 31, the operations further comprising causingthe heart condition to be communicated to a user.

33. The method of example 31 or example 32, wherein an abnormal heartcondition is determined based on the second atrial depolarizationmeasure being greater than the first atrial depolarization measure.

34. The method of any one of examples 23-33, wherein theelectrocardiograms and respective atrial depolarization measures includeelectrocardiograms and atrial depolarization measures for at least onelead associated with a left side of the patient's heart and at least onelead associated with a right side of the patient's heart, the methodfurther comprising comparing a left side atrial depolarization indexdetermined based on the at least one atrial depolarization measuredetermined for the left side with a right side atrial depolarizationindex determined based on the at least one atrial depolarization measuredetermined for the right side.

35. The method of example 34, further comprising determining a heartcondition based on the comparison.

36. The method of example 35, the operations further comprising causingthe heart condition to be communicated to a user.

37. The method of example 35 or example 36, wherein an abnormal heartcondition is determined based on the right side atrial depolarizationindex being greater than the left side atrial depolarization index.

38. The method of any one of examples 23-37, wherein the left sideatrial depolarization index comprises an average over multiple atrialdepolarization measures, corresponding to extrema at a selected one ofthe points in time, determined based on electrocardiograms measured formultiple respective leads associated with the left side, and the rightside atrial depolarization index is determined by averaging overmultiple atrial depolarization measures, corresponding to extrema at theselected point in time, determined based on electrocardiograms measuredfor multiple respective leads associated with the right side.

39. The method of example 38, wherein the at least one atrialdepolarization index comprises an average over two or more atrialdepolarization measures.

40. The method of example 39, wherein the average is taken over two ormore heart beats.

41. The method of example 38 or example 39, wherein the average is takenover two or more leads.

42. The method of any one of examples 23-41, wherein the at least oneatrial depolarization index comprises an adjustment factor that is basedon at least one of an age or a gender of the patient.

43. The method of any one of examples 23-42, wherein the at least oneatrial depolarization index is based on the at least one atrialdepolarization measure and a heart rate of the patient.

44. The method of any one of examples 23-43, wherein the time-frequencytransform comprises a continuous wavelet transform and thetime-frequency map comprises a scalogram.

45. The method of any one of examples 23-44, wherein the time-frequencymaps are absolute-value maps.

46. The method of any one of examples 23-45, further comprisingdetermining a heart condition based on a comparison of the at least oneatrial depolarization index against a threshold.

47. The method of example 46, further comprising causing the heartcondition to be communicated to a user.

48. The method of example 46, wherein an abnormal heart condition isdetermined based on the at least one atrial depolarization index beingbelow the threshold.

49. The method of any one of examples 23-48, wherein the outputtingcomprises causing the at least one atrial depolarization index to bedisplayed in a user interface.

50. One or more machine-readable media storing instructions that, whenexecuted by one or more processors, cause the one or more processors toperform the method of any one of examples 23-49.

51. A system comprising an electrode interface configured to receive oneor more electrocardiogram signals via one or more respective electrodesoperatively connected to the electrode interface; and a processingfacility communicatively coupled to the electrode interface andconfigured to perform the method of any one of examples 23-49.

Although the invention has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader scope of the invention. Accordingly, the specification anddrawings are to be regarded in an illustrative rather than a restrictivesense.

1. A method comprising: using one or more hardware processors,converting one or more electrocardiograms associated with one or morerespective leads by time-frequency transform into one or more respectivetwo-dimensional time-frequency maps; and, for a selected type ofwaveform, identifying the waveform of the selected type within the oneor more time-frequency maps, determining one or more morphologies of theidentified waveform within the one or more time-frequency maps,analyzing the one or more time-frequency maps based at least in part onthe one or more morphologies of the identified waveform to determine oneor more extrema associated with the identified waveform within the oneor more time-frequency maps, and determining a disease condition basedon at least one of one or more amplitude measures, one or more frequencymeasures, or one or more time measures of the determined one or moreextrema.
 2. The method of claim 1, wherein analyzing the one or moretime-frequency maps comprises analyzing at least one of the one or moretime-frequency maps across time at one or more frequencies or within oneor more frequency ranges selected based at least in part on a respectiveat least one morphology of the waveform to determine one or more pointsin time associated with the selected waveform, and determining the oneor more extrema across frequency at the determined one or more in time.3. The method of claim 1, wherein analyzing the one or moretime-frequency maps comprises determining the one or more extrema acrosstime and frequency in the one or more time-frequency maps within a timeinterval associated with the waveform.
 4. The method of claim 1, whereinanalyzing the one or more time-frequency maps comprises determining,from the respective one or more electrocardiograms, one or more pointsin time associated with the waveform, and determining the one or moreextrema across frequency at the determined one or more points in time.5. The method of claim 1, wherein one or more numbers of extremadetermined in the one or more time-frequency maps depend on therespective one or more morphologies of the waveform.
 6. The method ofclaim 1, wherein determining the disease condition comprises comparingat least one of the one or more amplitude measures, one or morefrequency measures, or one or more time measures of the one or moreextrema against one or more respective empirical thresholds and basingthe determination of the disease condition on the comparison.
 7. Themethod of claim 6, wherein determining the disease condition comprisesdetecting atrial enlargement based at least in part on at least one of:comparisons of the amplitude measures and frequencies of extrema of abiphasic P wave in lead V1 against respective empirical amplitude andfrequency thresholds associated with a normal biphasic P wave in leadV1; or comparison of the sum of inverse frequencies of a double-peakedmonophasic P wave in lead II against an empirical threshold associatedwith a normal monophasic P wave in lead II.
 8. The method of claim 1,wherein the disease condition is determined based at least in part on atime-frequency map for which extrema are determined at at least twopoints in time, and wherein determining the disease condition comprisescomparing the extrema at the at least two points in time against eachother and basing the determination of the disease condition on thecomparison.
 9. The method of claim 8, wherein determining the diseasecondition comprises detecting atrial enlargement based at least in parton at least one of: comparisons of amplitude measures between extrema ofa biphasic P wave in lead V1; or comparisons of amplitude measuresbetween extrema of a double-peaked monophasic P wave in lead II.
 10. Themethod of claim 8, wherein determining the disease condition comprisesdetecting ventricular hypertrophy based on lead-dependent comparisons ofamplitude measures between extrema associated with a biphasic RS segmentin any of leads V1, V2, V3, V4, V5, or V6.
 11. The method of claim 1,further comprising normalizing each of the one or more time-frequencymaps based at least in part on a difference between a maximum and aminimum identified in the respective time-frequency map across time inan interval encompassing an RS segment and across frequency, wherein theextrema are determined from the normalized time-frequency maps.
 12. Themethod of claim 1, further comprising communicating the determineddisease condition to a user.
 13. One or more machine-readable mediastoring instructions for execution by one or more processors, theinstructions causing the one or more processors to perform operationscomprising: converting one or more electrocardiograms associated withone or more respective leads by time-frequency transform into one ormore respective two-dimensional time-frequency maps; and, for a selectedtype of waveform, identifying the waveform of the selected type withinthe one or more time-frequency maps, determining one or moremorphologies of the identified waveform within the one or moretime-frequency maps, analyzing the one or more time-frequency maps basedat least in part on the one or more morphologies of the identifiedwaveform to determine one or more extrema associated with the identifiedwaveform within the one or more time-frequency maps, and determining adisease condition based on at least one of one or more amplitudemeasures, one or more frequency measures, or one or more time measuresof the determined one or more extrema.
 14. The one or moremachine-readable media of claim 13, wherein analyzing the one or moretime-frequency maps comprises analyzing at least one of the one or moretime-frequency maps across time at one or more frequencies or within oneor more frequency ranges selected based at least in part on a respectiveat least one morphology of the waveform to determine one or more pointsin time associated with the selected waveform, and determining the oneor more extrema across frequency at the determined one or more in time.15. The one or more machine-readable media of claim 13, whereinanalyzing the one or more time-frequency maps comprises determining,from the respective one or more electrocardiograms, one or more pointsin time associated with the waveform, and determining the one or moreextrema across frequency at the determined one or more points in time.16. The one or more machine-readable media of claim 13, whereinanalyzing the one or more time-frequency maps comprises determining theone or more extrema across time and frequency in the one or moretime-frequency maps within a time interval associated with the waveform.17. The one or more machine-readable media of claim 13, wherein one ormore numbers of extrema determined in the one or more time-frequencymaps depend on the respective one or more morphologies of the waveform.18. The one or more machine-readable media of claim 13, whereindetermining the disease condition comprises comparing at least one ofthe one or more amplitude measures, one or more frequency measures, orone or more time measures of the one or more extrema against one or morerespective empirical thresholds and basing the determination of thedisease condition on the comparison.
 19. The one or moremachine-readable media of claim 13, wherein the disease condition isdetermined based at least in part on a time-frequency map for whichextrema are determined at at least two points in time, and whereindetermining the disease condition comprises comparing the extrema at theat least two points in time against each other and basing thedetermination of the disease condition on the comparison.
 20. A devicecomprising: an electrode interface configured to receive one or moreelectrocardiogram signals via one or more respective electrodesoperatively connected to the electrode interface; and a processingfacility communicatively coupled to the electrode interface andconfigured to: generate one or more electrocardiograms for one or morerespective leads from the one or more electrocardiogram signals; convertthe one or more electrocardiograms by time-frequency transform into oneor more respective two-dimensional time-frequency maps; and, for aselected type of waveform, identify the waveform of the selected typewithin the one or more time-frequency maps, determine one or moremorphologies of the identified waveform within the one or moretime-frequency maps, analyze the one or more time-frequency maps basedat least in part on the one or more morphologies of the identifiedwaveform to determine one or more extrema associated with the identifiedwaveform within the one or more time-frequency maps, and determining adisease condition based on at least one of one or more amplitudemeasures, one or more frequency measures, or one or more time measuresof the determined one or more extrema.
 21. The device of claim 20,further comprising a display operatively displaying the determineddisease condition to the user.